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    <abstract>Recent research has increasingly focused on user-generated content to clarify opinions expressed in social media discourse. The Actor and Topic-Aware Stance Detection in Public Discourse challenge encourages research on stance detection in polarized social media discourse on the Palestinian–Israeli conflict. The challenge comprises two subtasks: one for actor-level alignments and the other for cross-topic generalization patterns. The StanceNakba2026 task includes two subtasks: (A) Actor-Level Stance Detection in English and (B) Cross-Topic Stance Detection in Arabic. Our team participated in both subtasks with the name “Viva_Palestine”. In Subtask A, the proposed method is based on the Bert-Base-Uncased model and achieved a Macro F1-score of 0.9190, placing 6th out of 13 teams. In Subtask B, the proposed method is based on the MARBERT model and achieved a Macro F1-score of 0.8724 (the top rank in the leaderboard), placing first out of 10 teams. These results show that the proposed modelling method performs well for both entity-specific stance alignment and strong cross-topic generalization.</abstract>
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%0 Conference Proceedings
%T Viva_Palestine at StanceNakba Shared Task: Actor and Topic-Aware Stance Detection in Public Discourse
%A El-Kassas, Wafaa
%A Khalil, Enas
%A El Houby, Enas
%Y Jarrar, Mustafa
%Y El-Haj, Mo
%Y Haddad, Amal
%Y Atiani, Serin
%Y Abudalfa, Shadi
%Y Regier, Terry
%Y Rayson, Paul
%Y Sima’an, Khalil
%Y Mansour, Camille
%S Proceedings of the 2nd International Workshop on Nakba Narratives as Language Resources @ LREC 2026
%D 2026
%8 May
%I ELRA Language Resources Association (ELRA)
%C Palma, Mallorca (Spain)
%F el-kassas-etal-2026-viva
%X Recent research has increasingly focused on user-generated content to clarify opinions expressed in social media discourse. The Actor and Topic-Aware Stance Detection in Public Discourse challenge encourages research on stance detection in polarized social media discourse on the Palestinian–Israeli conflict. The challenge comprises two subtasks: one for actor-level alignments and the other for cross-topic generalization patterns. The StanceNakba2026 task includes two subtasks: (A) Actor-Level Stance Detection in English and (B) Cross-Topic Stance Detection in Arabic. Our team participated in both subtasks with the name “Viva_Palestine”. In Subtask A, the proposed method is based on the Bert-Base-Uncased model and achieved a Macro F1-score of 0.9190, placing 6th out of 13 teams. In Subtask B, the proposed method is based on the MARBERT model and achieved a Macro F1-score of 0.8724 (the top rank in the leaderboard), placing first out of 10 teams. These results show that the proposed modelling method performs well for both entity-specific stance alignment and strong cross-topic generalization.
%R 10.63317/2qfbhttoue49
%U https://aclanthology.org/2026.nakbanlp-1.36/
%U https://doi.org/10.63317/2qfbhttoue49
%P 239-243

Markdown (Informal)

[Viva_Palestine at StanceNakba Shared Task: Actor and Topic-Aware Stance Detection in Public Discourse](https://aclanthology.org/2026.nakbanlp-1.36/) (El-Kassas et al., NakbaNLP 2026)

• Viva_Palestine at StanceNakba Shared Task: Actor and Topic-Aware Stance Detection in Public Discourse (El-Kassas et al., NakbaNLP 2026)

ACL

• Wafaa El-Kassas, Enas Khalil, and Enas El Houby. 2026. Viva_Palestine at StanceNakba Shared Task: Actor and Topic-Aware Stance Detection in Public Discourse. In Proceedings of the 2nd International Workshop on Nakba Narratives as Language Resources @ LREC 2026, pages 239–243, Palma, Mallorca (Spain). ELRA Language Resources Association (ELRA).